Data-driven jury selection software for plaintiff litigation attorneys. SIGNIFICANT CONTROVERSY: Vice News (Mar 2020) reported Momus Analytics’ algorithm uses race as a factor in scoring jurors. BC Law Review (cited by 20) confirms ‘predictive scoring system is using race to grade potential jurors.’ Georgetown MCRP Journal: ‘data-driven legal intervention that experts say falls short.’ Listed in AIAAIC Repository as an AI bias incident. LexisNexis equity report cites as example of AI equity concerns in legal systems. The company’s algorithm is proprietary and opaque — no public details on how juror scores are calculated. Tool deploys web-scraping bots to collect juror social media data (Jurvantis AI, Texas Bar CLE). Judges are ‘scrambling for guardrails’ around algorithmic jury selection. Deliberately restricts access to plaintiff firms only — defense attorneys cannot use the tool, creating information asymmetry in the courtroom. Founded by a plaintiff attorney who uses it in his own cases (disclosed in Philip Morris litigation, Florida 3rd DCA 2021). CTO Jorge Fonte. Named NLJ ‘2020 Emerging Legal Technologies.’ CIO Applications ‘Top Data Science and Analytics 2026.’ MOMUS 2.0 launched. 456 LinkedIn followers, 5 employees. No G2/Capterra reviews. No pricing publicly available. No security documentation despite handling sensitive juror personal data including scraped social media. Source diversity unusually strong for a tier 3 vendor: Vice News, BC Law Review, Georgetown, LexisNexis, court filings, NLJ, CIO Applications — tool attracts both recognition and controversy.
Company Info
- Founded: 2017
- Team size: 1-10 employees
- HQ: United States
- Sector: Legal Research
What We Haven’t Verified
This page was assembled from publicly available information. Feature claims and workflow mappings are based on what the vendor and third-party listings publish — not hands-on testing or practitioner feedback.
Workflows
Based on practitioner evidence, Momusanalytics is used in these workflows:
What practitioners struggle with
Real frustrations from legal professionals — the problems Momusanalytics addresses (or should address). Sourced from practitioner reviews, Reddit threads, and case studies.
Litigation team preparing for trial needs to understand how a specific judge rules on summary judgment motions, Daubert challenges, and sentencing — but there's no systematic analytics on judge behavior, so strategy relies on anecdotes from colleagues who've appeared before that judge
Defense team is preparing for trial in 3 weeks and needs to build a coherent timeline from fragmented evidence — witness statements contradict each other, body cam timestamps don't align, and critical connections between defendants are buried across thousands of documents
Where it fits in your workflow
Community Data
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